Search

Lignin is a rich renewable aromatic resource that can produce high-value-added chemicals. Lignin is regarded as one of the three major components of lignocellulosic biomass, which is composed of phenylpropane units connected by CO bond and CC bond. The cleavage of two chemical bonds is the main catalytic pathway in the production of chemicals and fuels from lignin. Although the cleavage of CO converts lignin into valuable aromatic compounds and renewable carbon sources, selective depolymerization for CC bonds is an important method to increase the yield of aromatic monomers. Therefore, in this review, we summarized the latest research trends on CC bond selective cleavage in lignin and lignin model compounds, focusing on various catalytic systems, including hydrogenolysis, oxidate, photocatalysis, and electrocatalysis. By analyzing the current status of CC bond breakage, the core issues and challenges related to this process and the expectations for future research were emphasized.

Abstract We investigated the potential correlation between miR‐223 and NAcHT, LRR, and PYd domain‐containing protein 3 (NLRP3) in the context of renal ischemia‐reperfusion injury (RIRI), which is a leading cause of acute renal failure with significant mortality rates. Additionally, miR‐223 has been implicated in renal inflammation, further highlighting its relevance to this study. C57BL/6 male mice were used as RIRI models. After successful modeling, pathological examinations and serum creatinine and miR‐223 levels were tested. Pro‐inflammatory cytokine (IL‐1β, IL‐6, IL‐8, NLPR3, TLR4) expression was detected in mice by western blot (kidney tissue) and enzyme‐linked immunosorbent assay (serum). HK‐2 cells were used for in vitro experiments. A hypoxia/reoxygenation (H/R) model was used, and miR‐223 and pro‐inflammatory cytokine levels were detected using PCR and western blot assays, respectively. A dual‐luciferase reporter assay was conducted to confirm the binding of miR‐223 to NLPR3. Next, NLRP3 was knocked down to determine whether the anti‐inflammatory function of miR‐223 is dependent on NLRP3. MiR‐223 expression was lower in RIRI mice than in the sham operation group. The level of miR‐223 negatively correlated with serum creatinine levels and the severity of tubule injury. Increased proinflammatory cytokine levels in RIRI mice were observed. In vitro, miR‐223 alleviated the inflammatory response in H/R treated cells by inhibiting proinflammatory cytokines. Dual‐luciferase reporter and western blot assays confirmed the binding of miR‐223 to NLRP3. NLRP3 knockdown reversed the anti‐inflammatory effects of miR‐223 in HK‐2 cells. MiR‐223 plays an anti‐inflammatory role in RIRI by targeting NLRP3 to repress pro‐inflammatory factors.

Converting carbohydrates into 5-hydroxymethylfurfural (5-HMF) is an attractive and promising route for value-added utilization of agricultural and forestry biomass resource. As an important platform compound, 5-HMF possesses high active furan structure with hydroxymethyl and aldehyde group for production of various bio-chemicals and materials, meanwhile, which suffer from low stability and poor yield during the industrial biorefinery process. Hence, selective production of 5-HMF with high-yield and low-cost has attracted extensive attention from scientific and industrial researchers. This review sorted and described the latest advanced research on solvent and catalyst system, as well as energy field effect for production of 5-HMF with different feedstock in detail, emphatically discussing the solvent effect and its synergistic effect with other aspects. Besides, the future prospects and challenges for production of 5-HMF from carbohydrates were also presented, which provide a profound insight into industrial 5-HMF process with economic and environmental feature.

Abstract This study presents a high-accuracy, all-fiber mode division multiplexing (MDM) reconstructive spectrometer (RS). The MDM was achieved by utilizing a custom-designed 3 × 1 mode-selective photonics lantern to launch distinct spatial modes into the multimode fiber (MMF). This facilitated the information transmission by increasing light scattering processes, thereby encoding the optical spectra more comprehensively into speckle patterns. Spectral resolution of 2 pm and the recovery of 2000 spectral channels were accomplished. Compared to methods employing single-mode excitation and two-mode excitation, the three-mode excitation method reduced the recovered error by 88% and 50% respectively. A resolution enhancement approach based on alternating mode modulation was proposed, reaching the MMF limit for the 3 dB bandwidth of the spectral correlation function. The proof-of-concept study can be further extended to encompass diverse programmable mode excitations. It is not only succinct and highly efficient but also well-suited for a variety of high-accuracy, high-resolution spectral measurement scenarios. Graphical Abstract

Ulcerative colitis (UC) is a chronic intestinal disorder characterized by severe intestinal inflammation and superficial mucosal ulcers. Ramulus mori alkaloids (SZ-A) were approved by the National Medical Products Administration of China in 2020 for the treatment of type 2 diabetes mellitus. Later studies confirmed that SZ-A act on multiple inflammatory pathways to exert anti-inflammatory effects, suggesting that SZ-A has the potential to alleviate UC. A temperature-sensitive hydrogel embedded with sodium alginate (SA)-chitosan (CS) microspheres was constructed for colon-targeted delivery of SZ-A. The results showed that the microspheres prepared by exogenous emulsification had rounded and smooth surfaces with high drug-loading capacity. The optimum preparation conditions were as follows: 600 rpm stirring speed; a 1:4 ratio of water-to-oil phase; and a 5% Span80 emulsifier. The temperature-sensitive hydrogels embedded with SA-CS microspheres (TMH) had a moderate gelling temperature of 31.8 ± 0.74°C. The rheologic results showed that the internal structure of TMH was stable with viscoelasticity, and the in vitro release results indicated that the TMH exhibited slow-release performance. The findings suggest that TMH has suitable gelling temperature and viscoelasticity, and may be a promising targeted delivery vector for UC treatment.

Abstract Non-small cell lung cancer (NSCLC) is a common and aggressive primary malignancy worldwide. Dysregulation of long non-coding RNAs (lncRNAs) has been shown to play an essential regulatory role in multiple cancers. However, the role of PGM5-AS1 in NSCLC remains unclear. Here, we found that PGM5-AS1 was down-regulated in NSCLC tissues and cells. Furthermore, reduced PGM5-AS1 expression levels were associated with larger tumor size, positive lymph node metastasis, advanced TNM stage and worse prognosis. We found that overexpression of PGM5-AS1 inhibited cell proliferation and metastasis, and induced apoptosis and G0/G1 cell cycle arrest in NSCLC cell lines. Using dual luciferase gene reporter and RNA immunoprecipitation assays, we confirmed that miR-423-5p interacted with PGM5-AS1, and that their expression levels were negatively correlated in NSCLC tissues. miR-423-5p was also found to reverse PGM5-AS1-induced malignant biological behavior. Moreover, we identified slit guidance ligand 2 (SLIT2) as a target gene of miR-423-5p. Using a dual luciferase gene reporter assay, we confirmed the regulatory relationship between SLIT2 and miR-423-5p and demonstrated that their expression levels were negatively correlated. Our rescue experiments showed that SLIT2 knockdown reversed miR-423-5p-mediated effects. Overall, this study identifies PGM5-AS1 as a potential prognostic biomarker for NSCLC and shows that PGM5-AS1 suppresses NSCLC development by regulating the miR-423-5p/SLIT2 axis.

Abstract Background Our previous studies have indicated that mRNA and protein levels of PPIH are significantly upregulated in Hepatocellular Carcinoma (LIHC) and could act as predictive biomarkers for patients with LIHC. Nonetheless, the expression and implications of PPIH in the etiology and progression of common solid tumors have yet to be explored, including its potential as a serum tumor marker. Methods We employed bioinformatics analyses, augmented with clinical sample evaluations, to investigate the mRNA and protein expression and gene regulation networks of PPIH in various solid tumors. We also assessed the association between PPIH expression and overall survival (OS) in cancer patients using Kaplan-Meier analysis with TCGA database information. Furthermore, we evaluated the feasibility and diagnostic efficacy of PPIH as a serum marker by integrating serological studies with established clinical tumor markers. Results Through pan-cancer analysis, we found that the expression levels of PPIH mRNA in multiple tumors were significantly different from those in normal tissues. This study is the first to report that PPIH mRNA and protein levels are markedly elevated in LIHC, Colon adenocarcinoma (COAD), and Breast cancer (BC), and are associated with a worse prognosis in these cancer patients. Conversely, serum PPIH levels are decreased in patients with these tumors (LIHC, COAD, BC, gastric cancer), and when combined with traditional tumor markers, offer enhanced sensitivity and specificity for diagnosis. Conclusion Our findings propose that PPIH may serve as a valuable predictive biomarker in tumor patients, and its secreted protein could be a potential serum marker, providing insights into the role of PPIH in cancer development and progression.

ObjectivesThis study aimed to investigate the level of acceptance of family doctors (FDs) exhibited by residents in China.MethodsA cross-sectional study based on a structured self-administered questionnaire was conducted to investigate residents in eastern, central, and western China between September and December 2021. A multivariable stepwise logistic regression model was employed to identify the factors associated with health-seeking behavior after the signing of agreements concerning family doctor contract services (FDCS) as well as residents’ willingness to change FDs.ResultsAmong the 2,394 respondents included in this research, 55.8% sought primary care from their FDs when they became ill, whereas 9.7% expressed a willingness to change FDs. Residents who reported high levels of satisfaction with FDCS [odds ratio (OR) = 2.162] and trust in FDs (OR = 1.430) were more likely to seek initial help from FDs. In addition, residents from central China (OR = 0.546) and western China (OR = 0.704) and those who exhibited a high level of trust in FDs (OR = 0.238) were less likely to change FDs.ConclusionThe level of FD acceptance among Chinese residents was relatively high. Satisfaction with FDCS and trust in FDs were associated with the acceptance of FDs among residents. FDs should make efforts to enhance the quality of health services as well as the overall health experience of residents.

Abstract Background Schizophrenia (SCZ) is a profound mental disorder with a multifactorial etiology, including genetics, environmental factors, and demographic influences such as ethnicity and geography. Among these, the studies of SCZ also shows racial and regional differences. Methods We first established a database of biological samples for SCZ in China’s ethnic minorities, followed by a serum metabolomic analysis of SCZ patients from various ethnic groups within the same region using the LC-HRMS platform. Results Analysis identified 47 metabolites associated with SCZ, with 46 showing significant differences between Miao and Han SCZ patients. These metabolites, primarily fatty acids, amino acids, benzene, and derivatives, are involved in fatty acid metabolism pathways. Notably, L-Carnitine, L-Cystine, Aspartylphenylalanine, and Methionine sulfoxide demonstrated greater diagnostic efficacy in Miao SCZ patients compared to Han SCZ patients. Conclusion Preliminary findings suggest that there are differences in metabolic levels among SCZ patients of different ethnicities in the same region, offering insights for developing objective diagnostic or therapeutic monitoring strategies that incorporate ethnic considerations of SCZ.

Antibody-drug conjugates (ADCs) are a new type of targeting antibodies that conjugate with highly toxic anticancer drugs via chemical linkers to exert high specificity and efficient killing of tumor cells, thereby attracting considerable attention in precise oncology therapy. Cetuximab (Cet) is a typical antibody that offers the benefits of good targeting and safety for individuals with advanced and inoperable cutaneous squamous cell carcinoma (cSCC); however, its anti-tumor activity is limited to a single use. Cisplatin (CisPt) shows good curative effects; however, its adverse effects and non-tumor-targeting ability are major drawbacks. In this study, we designed and developed a new ADC based on a new cytotoxic platinum (IV) prodrug (C8Pt(IV)) and Cet. The so-called antibody-platinum (IV) prodrugs conjugates, named Cet-C8Pt(IV), showed excellent tumor targeting in cSCC. Specifically, it accurately delivered C8Pt(IV) into tumor cells to exert the combined anti-tumor effect of Cet and CisPt. Herein, metabolomic analysis showed that Cet-C8Pt(IV) promoted cellular apoptosis and increased DNA damage in cSCC cells by affecting the vitamin B6 metabolic pathway in tumor cells, thereby further enhancing the tumor-killing ability and providing a new strategy for clinical cancer treatment using antibody-platinum (IV) prodrugs conjugates.

Abstract CD8+ exhausted T cells (CD8+ Tex) played a vital role in the progression and therapeutic response of cancer. However, few studies have fully clarified the characters of CD8+ Tex related genes in ovarian cancer (OC). The CD8+ Tex related prognostic signature (TRPS) was constructed with integrative machine learning procedure including 10 methods using TCGA, GSE14764, GSE26193, GSE26712, GSE63885 and GSE140082 dataset. Several immunotherapy benefits indicators, including Tumor Immune Dysfunction and Exclusion (TIDE) score, immunophenoscore (IPS), TMB score and tumor escape score, were used to explore performance of TRPS in predicting immunotherapy benefits of OC. The TRPS constructed by Enet (alpha = 0.3) method acted as an independent risk factor for OC and showed stable and powerful performance in predicting clinical outcome of patients. The C-index of the TRPS was higher than that of tumor grade, clinical stage, and many developed signatures. Low TRPS score indicated a higher level of CD8+ T cell, B cell, macrophage M1, and NK cells, representing a relative immunoactivated ecosystem in OC. OC patients with low risk score had a higher PD1&CTLA4 immunophenoscore, higher TMB score, lower TIDE score and lower tumor escape score, suggesting a better immunotherapy response. Moreover, higher TRPS score indicated a higher score of cancer-related hallmarks, including angiogenesis, EMT, hypoxia, glycolysis, and notch signaling. Vitro experiment showed that ARL6IP5 was downregulated in OC tissues and inhibited tumor cell proliferation. The current study constructed a novel TRPS for OC, which could serve as an indicator for predicting the prognosis, immune infiltration and immunotherapy benefits for OC patients.

Background Pelvic organ prolapse (POP), characterised by the downward displacement of pelvic organs, is a prevalent disorder that affects adult women. This study explored the therapeutic potential of PX-478, a selective hypoxia-inducible factor-1α (HIF-1α) inhibitor, in a murine POP model.Methods A murine POP model was established through ovariectomy, mimicking oestrogen deprivation. Fifteen C57BL/6J mice were randomly assigned to control, POP, and PX-478 groups. PX-478, targeting HIF-1α, was administered intravaginally. The analysis of fibroblasts, macrophage and inflammation was performed through Masson staining, immunofluorescence, and ELISA. Collagen distribution was assessed using Sirius Red staining. Expression levels of matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMP-1) were determined through immunohistochemistry and western blot. Fibroblast proliferation and apoptosis were evaluated by CCK-8 assay and flow cytometry.Results PX-478 treatment significantly reduced vaginal length, indicating a therapeutic effect on POP severity. Masson staining revealed reduced fibrotic changes and collagen disruption in PX-478-treated mice. Immunofluorescence showed increased fibroblast markers (Vimentin, α-SMA) and collagen fibres by PX-478. Sirius Red staining indicated PX-478 mitigated damage to Type I and Type III collagen fibres. PX-478 significantly reduced MMP-2 and MMP-9 expression while increased TIMP-1. In macrophages, PX-478 decreased M1 and M2 markers (CD80, CD206) and IL-18 secretion. Fibroblasts exhibited increased proliferation, reduced apoptosis, and altered MMP/TIMP expression under PX-478 influence.Conclusion PX-478 demonstrates a therapeutic potential in the mice POP model. It reduces vaginal length, attenuates fibrosis, and modulates collagen synthesis. Its immunomodulation is evident through reduced M1 and M2 macrophages and suppressed IL-18 secretion.

BackgroundThe intricate relationship among gut microbiota, serum metabolites, and immunophenotypes may significantly impact myocarditis. However, direct causal links between these domains and myocarditis are not well understood.MethodsThe study performed Mendelian randomization (MR) analysis using genetic data from public sources. Exposure data included 211 gut microbiota, 486 serum metabolites, and 731 immunophenotypes from Mibiogen, the Metabolomics GWAS server, and GWAS catalog databases. Single nucleotide polymorphisms (SNPs) were selected as instrumental variables based on established criteria. Myocarditis data from GWAS (427,911 participants, 24, 180, 570 SNPs) were used as the outcome variable. MR analysis was conducted using Inverse Variance Weighting (IVW), with Cochran’s Q test for heterogeneity and Egger’s intercept to assess horizontal pleiotropy.Results9 gut microbiota, 10 serum metabolites, and 2 immunophenotypes were negatively associated with myocarditis risk. In contrast, 5 gut microbiota, 12 serum metabolites, and 7 immunophenotypes were positively associated with myocarditis risk (all, P < 0.05). Sensitivity analyses confirmed the stability of these results.ConclusionThis MR study suggests that gut microbiota, serum metabolites, and immunophenotypes may causally influence myocarditis risk. These findings provide genetic evidence for myocarditis etiology and could inform future precision prevention and treatment strategies.

Breast cancer remains a leading cause of mortality in women worldwide. Triple-negative breast cancer (TNBC) is a particularly aggressive subtype characterized by rapid progression, poor prognosis, and lack of clear therapeutic targets. In the clinic, delineation of tumor heterogeneity and development of effective drugs continue to pose considerable challenges. Within the scope of our study, high heterogeneity inherent to breast cancer was uncovered based on the landscape constructed from both tumor and healthy breast tissue samples. Notably, TNBC exhibited significant specificity regarding cell proliferation, differentiation, and disease progression. Significant associations between tumor grade, prognosis, and TNBC oncogenes were established via pseudotime trajectory analysis. Consequently, we further performed comprehensive characterization of the TNBC microenvironment. A crucial epithelial subcluster, E8, was identified as highly malignant and strongly associated with tumor cell proliferation in TNBC. Additionally, epithelial-mesenchymal transition (EMT)-associated fibroblast and M2 macrophage subclusters exerted an influence on E8 through cellular interactions, contributing to tumor growth. Characteristic genes in these three cluster cells could therefore serve as potential therapeutic targets for TNBC. The collective findings provided valuable insights that assisted in the screening of a series of therapeutic drugs, such as pelitinib. We further confirmed the anti-cancer effect of pelitinib in an orthotopic 4T1 tumor-bearing mouse model. Overall, our study sheds light on the unique characteristics of TNBC at single-cell resolution and the crucial cell types associated with tumor cell proliferation that may serve as potent tools in the development of effective anti-cancer drugs.

Efficiency, safety and cost are three major evaluation indexes of warehouse operation. However, the uncertainty of efficiency, safety and cost factors will lead to economic losses and waste of resources. The purpose of this study is to propose a novel parameterized neutrosophic objective–proportionate genetic algorithm model (PNO–PGA) to optimize the above three objectives. There are three main contributions of this study. Firstly, a novel score function of neutrosophic sets (NSs) is proposed to effectively integrate the fuzziness of efficiency, safety and cost to avoid the evaluation result being too idealized. Secondly, a novel proportionate genetic algorithm is applied to adaptively realize the iteration and inheritance processes. Finally, two parameters are proposed to make the algorithm model flexibly adapt to different types of environments and problems. Then, an example is used to compare the new method with genetic algorithm (GA). The result shows that PNO-PGA has better problem-solving ability in warehouse operation than GA.

In various fuzzy multiple attribute decision making (MADM) applications, different information descriptions and aggregation operators (AOs) play a crucial role. However, both the Einstein sum and product can include their typical algebraic operation features, but they lack the characteristics of periodicity operations. To fill the research gap of Einstein AOs for single-valued neutrosophic values (SvNVs), this article aims to propose Einstein AOs of sine SvNVs and their MADM model as their new extension. In this study, we first define a new sine SvNV, which integrates sine functions into the membership functions of indeterminacy, falsehood, and truth, and the Einstein operation laws of sine SvNVs. Then, we present the sine SvNV Einstein weighted average and geometric AOs and their properties. Furthermore, we develop a MADM model based on the proposed Einstein AOs in a SvNV circumstance. Lastly, we apply the developed MADM model to a site selection example of a hydrogen power plant as the verification of its application in a SvNV circumstance. The decision results reveal the rationality and validity of the developed model with respect to the comparison of the related models.

Phenanthroindolizidine alkaloids (PAs) are a series of compounds that have been isolated from traditional herbal medicines and have significant therapeutic potential, such as anti-arthritic, anti-viral, anti-inflammatory, and anti-glioma effects in vitro and in vivo . This study aimed to predict the absorption, distribution, metabolism, excretion, and toxicity (ADMET) characteristics of 44 compounds in silico and to verify the ADMET characteristics. The 2-dimensional structures of these compounds were generated using ChemDraw and the characteristics were predicted using ADMET Predictor™ software. Key characteristics, such as p K a , log P /log D , solubility, permeability, absolute bioavailability in rats, and preliminary toxicity, were measured on some typical compounds to verify the accuracy of the prediction results. The results showed that ADMET predicts physicochemical and biological properties quickly and accurately for PAs. PAs are biopharmaceutics classification system (BCS) class IV compounds with low bioavailability. Moreover, these compounds have higher lipophilicity and are easily distributed into the brain after oral administration to treat brain diseases. However, some of these compounds exhibited colonic toxicity. To improve the drug-like availability of these compounds, more in-depth research should be conducted on drug delivery systems.

[Objective] During the normal operation of metro lines, due to incomplete rail-ground insulation, metro that adopts AC (alternating current) traction power supply system can cause electromagnetic interference on adjacent metallic oil and gas pipelines. Therefore, it is necessary to study the external electromagnetic interference problem caused by metro AC traction power supply system. [Method] Taking the parallel section of a metallic oil and gas pipeline in Guangzhou and Guangzhou Metro Line 22 as example, a coupled model of the metro tunnel and the pipeline is established to simulate and analyze electromagnetic interference on the pipeline. The electromagnetic induction voltages, ground potentials, and coating tolerance voltages of different segments of the oil and gas pipeline with varying angles to the tunnel are calculated, and the main influencing factors of electromagnetic interference are analyzed. [Result & Conclusion] Under the format of AC traction power supply system, the actual voltage endured by the pipeline coating is the difference between the voltage on the surface of the internal metal pipeline and the ground potential in the external soil. The induced voltage amplitude generated by the metro tunnel on metallic pipeline surface is more than 10 times the amplitude of soil ground potential, with the electromagnetic induction voltage between the pipeline and the metro being the main factor causing electromagnetic interference on the metallic oil and gas pipeline. The parallel section of the metallic oil and gas pipeline and the metro tunnel is the main area generating electromagnetic interference. Adding return line can significantly reduce the electromagnetic induction voltage on the metallic oil and gas pipeline, and the position and quantity of the return line have a significant impact on reducing electromagnetic interference.

Calcium-based biomaterials have been intensively studied in the field of drug delivery owing to their excellent biocompatibility and biodegradability. Calcium-based materials can also deliver contrast agents, which can enhance real-time imaging and exert a Ca2+-interfering therapeutic effect. Based on these characteristics, amorphous calcium carbonate (ACC), as a brunch of calcium-based biomaterials, has the potential to become a widely used biomaterial. Highly functional ACC can be either discovered in natural organisms or obtained by chemical synthesis However, the standalone presence of ACC is unstable in vivo. Additives are required to be used as stabilizers or core–shell structures formed by permeable layers or lipids with modified molecules constructed to maintain the stability of ACC until the ACC carrier reaches its destination. ACC has high chemical instability and can produce biocompatible products when exposed to an acidic condition in vivo, such as Ca2+ with an immune-regulating ability and CO2 with an imaging-enhancing ability. Owing to these characteristics, ACC has been studied for self-sacrificing templates of carrier construction, targeted delivery of oncology drugs, immunomodulation, tumor imaging, tissue engineering, and calcium supplementation. Emphasis in this paper has been placed on the origin, structural features, and multiple applications of ACC. Meanwhile, ACC faces many challenges in clinical translation, and long-term basic research is required to overcome these challenges. We hope that this study will contribute to future innovative research on ACC.

Abstract Development of a high power fiber laser at special waveband, which is difficult to achieve by conventional rare-earth-doped fibers, is a significant challenge. One of the most common methods for achieving lasing at special wavelength is Raman conversion. Phosphorus-doped fiber (PDF), due to the phosphorus-related large frequency shift Raman peak at 40 THz, is a great choice for large frequency shift Raman conversion. Here, by adopting 150 m large mode area triple-clad PDF as Raman gain medium, and a novel wavelength-selective feedback mechanism to suppress the silica-related Raman emission, we build a high power cladding-pumped Raman fiber laser at 1.2 μm waveband. A Raman signal with power up to 735.8 W at 1252.7 nm is obtained. To the best of our knowledge, this is the highest output power ever reported for fiber lasers at 1.2 μm waveband. Moreover, by tuning the wavelength of the pump source, a tunable Raman output of more than 450 W over a wavelength range of 1240.6–1252.7 nm is demonstrated. This work proves PDF’s advantage in high power large frequency shift Raman conversion with a cladding pump scheme, thus providing a good solution for a high power laser source at special waveband. Graphical Abstract

Abstract Background The pathogenesis of cardiac arrhythmias is multifaceted, encompassing genetic, environmental, hemodynamic, and various causative factors. Emerging evidence underscores a plausible connection between gut flora, serum metabolites, and specific types of arrhythmias. Recognizing the role of host genetics in shaping the microbiota, we employed two-sample Mendelian randomization analyses to investigate potential causal associations between gut flora, serum metabolites, and distinct arrhythmias. Methods Mendelian randomization methods were deployed to ascertain causal relationships between 211 gut flora, 575 serum metabolites, and various types of arrhythmias. To ensure the reliability of the findings, five complementary Mendelian randomization methods, including inverse variance weighting methods, were employed. The robustness of the results was scrutinized through a battery of sensitivity analyses, incorporating the Cochran Q test, leave-one-out test, and MR-Egger intercept analysis. Results Eighteen gut flora and twenty-six serum metabolites demonstrated associations with the risk of developing atrial fibrillation. Moreover, ten gut flora and fifty-two serum metabolites were linked to the risk of developing supraventricular tachycardia, while eight gut flora and twenty-five serum metabolites were associated with the risk of developing tachycardia. Additionally, six gut flora and twenty-one serum metabolites exhibited associations with the risk of developing bradycardia. Conclusion This study revealed the potential causal relationship that may exist between gut flora, serum metabolites and different cardiac arrhythmias and highlights the need for further exploration. This study provides new perspectives to enhance diagnostic and therapeutic strategies in the field of cardiac arrhythmias.

In the existing literature, there are few studies on the effect of deposition bias on the tribological properties of carbon-doped high-entropy alloy coatings. In order to further study the effect of the deposition bias on the properties of coatings, (AlTiVCrNb)CxNy coatings were deposited via unbalanced RF magnetron sputtering. The microstructure and tribological properties of carbon-doped high-entropy alloy ceramic coatings under different deposition biases were studied. The composition, morphology, crystal structure, and chemical morphology of each element of the coating were analyzed using scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). The hardness, elastic modulus, friction, and wear properties of the coating were further characterized using a nanoindentation instrument, reciprocating sliding friction, a wear tester, and a white light interferometer. The coating density reached the optimal level when the deposition bias value was 90 V. The hardness and elastic modulus of the (AlTiVCrNb)CxNy coating increased first and then decreased with an increase in deposition bias, and the maximum hardness was 23.98 GPa. When the deposition bias was 90 V, the coating formed a good-quality carbon transfer film on the surface of the counterbody due to sp2 clusters during the friction and wear process. The average friction coefficient and wear rate of the (AlTiVCrNb)CxNy coating were the lowest, 0.185 and 1.6 × 10−7 mm3/N·m, respectively. The microstructure, mechanical properties, and tribological performance of the (AlTiVCrNb)CxNy coating were greatly affected by the change in deposition bias, and an (AlTiVCrNb)CxNy coating with excellent structure and friction properties could be prepared using graphite co-sputtering.

Dendritic cells (DCs) are involved in the initiation and maintenance of immune responses against malignant cells by recognizing conserved pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs) through pattern recognition receptors (PRRs). According to recent studies, tumor cell-derived DNA molecules act as DAMPs and are recognized by DNA sensors in DCs. Once identified by sensors in DCs, these DNA molecules trigger multiple signaling cascades to promote various cytokines secretion, including type I IFN, and then to induce DCs mediated antitumor immunity. As one of the potential attractive strategies for cancer therapy, various agonists targeting DNA sensors are extensively explored including the combination with other cancer immunotherapies or the direct usage as major components of cancer vaccines. Moreover, this review highlights different mechanisms through which tumor-derived DNA initiates DCs activation and the mechanisms through which the tumor microenvironment regulates DNA sensing of DCs to promote tumor immune escape. The contributions of chemotherapy, radiotherapy, and checkpoint inhibitors in tumor therapy to the DNA sensing of DCs are also discussed. Finally, recent clinical progress in tumor therapy utilizing agonist-targeted DNA sensors is summarized. Indeed, understanding more about DNA sensing in DCs will help to understand more about tumor immunotherapy and improve the efficacy of DC-targeted treatment in cancer.

Obesity and related metabolic syndromes have been recognized as important disease risks, in which the role of adipokines cannot be ignored. Adiponectin (ADP) is one of the key adipokines with various beneficial effects, including improving glucose and lipid metabolism, enhancing insulin sensitivity, reducing oxidative stress and inflammation, promoting ceramides degradation, and stimulating adipose tissue vascularity. Based on those, it can serve as a positive regulator in many metabolic syndromes, such as type 2 diabetes (T2D), cardiovascular diseases, non-alcoholic fatty liver disease (NAFLD), sarcopenia, neurodegenerative diseases, and certain cancers. Therefore, a promising therapeutic approach for treating various metabolic diseases may involve elevating ADP levels or activating ADP receptors. The modulation of ADP genes, multimerization, and secretion covers the main processes of ADP generation, providing a comprehensive orientation for the development of more appropriate therapeutic strategies. In order to have a deeper understanding of ADP, this paper will provide an all-encompassing review of ADP.

As more devices are integrated into the Internet of Things (IoT) ecosystem, accurate and efficient information transmission between resource-constrained equipment has become particularly important. For data encryption, compared to traditional encryption schemes, elliptic curve cryptography (ECC) provides a higher security level with the same key size and invokes lower requirements in memory size, making ECC a desirable candidate. Since data transmission is inescapably suffered from electronic interference, communication attacks, and other elements, resulting in imperfections and inaccuracy of information transmission. Applying ECC in resource-constrained devices requires an error detection and correction mechanism, and it also needs to against side channel attacks. In this paper, an improved elliptic curve encryption scheme with an error detection and correction mechanism is proposed. Error detection is facilitated by an enhanced Montgomery ladder algorithm, while error correction is implemented through recomputation. Additionally, to safeguard against side-channel attacks, a technique of base point blinding via randomization is employed. This innovative scheme achieves a 100% error detection rate and offers robust defense against SSCA, DPA, RPA, ZPA, and Relative Doubling Attack. It has been demonstrated to surpass the efficiency and security of existing schemes, making it well-suited for deployment in devices with limited resources.

To overcome the limitations of both the conversion method based on the standard deviation and the decision flexibility in existing neutrosophic multi-valued decision-making models, this study aims to propose various new techniques including a conversion method, Aczel-Alsina aggregation operations, and a multi-attribute group decision making (MAGDM) model in the case of neutrosophic multi-valued sets (MVNSs). First, we propose a conversion method to convert neutrosophic multi-valued elements (MVNEs) into neutrosophic entropy elements (NEEs) based on the mean and normalized Shannon/probability entropy of truth, falsity, and indeterminacy sequences. Second, the score and accuracy functions of NEEs are defined for the ranking of NEEs. Third, the Aczel-Alsina t-norm and t-conorm operations of NEEs and the NEE Aczel-Alsina weighted arithmetic averaging (NEEAAWAA) and NEE Aczel-Alsina weighted geometric averaging (NEEAAWGA) operators are presented to reach the advantage of flexible operations by an adjustable parameter. Fourth, we propose a MAGDM model in light of the NEEAAWAA and NEEAAWGA operators and the score and accuracy functions in the case of NMVSs to solve flexible MAGDM problems with an adjustable parameter subject to decision makers’ preference. Finally, an illustrative example is given to verify the impact of different parameter values on the decision results of the proposed MAGDM model. Compared with existing techniques, the new techniques not only overcome the defects of existing techniques but also be broader and more versatile than existing techniques when dealing with MAGDM problems in the case of NMVSs.

The increase in installed PV capacity worldwide and the intermittent nature of solar resources highlight the importance of power prediction for grid integration of this technology. Therefore, there is an urgent need for an effective prediction model, but the choice of model hyperparameters greatly affects the prediction performance. In this paper, a multi-strategy improved snowmelt algorithm (MISAO) is proposed for optimizing intrinsic computing-expressive empirical mode decomposition with adaptive noise (ICEEMDAN) and weighted least squares support vector machine for PV power forecasting. Firstly, a cyclic chaotic mapping initialization strategy is used to generate a uniformly distributed high-quality population, which facilitates the algorithm to enter the appropriate search domain quickly. Secondly, the Gaussian diffusion strategy enhances the local exploration ability of the intelligences and extends their search in the solution space, effectively preventing them from falling into local optima. Finally, a stochastic follower search strategy is employed to reserve better candidate solutions for the next iteration, thus achieving a robust exploration–exploitation balance. With these strategies, the optimization performance of MISAO is comprehensively improved. In order to comprehensively evaluate the optimization performance of MISAO, a series of numerical optimization experiments were conducted using IEEE CEC2017 and test sets, and the effectiveness of each improvement strategy was verified. In terms of solution accuracy, convergence speed, robustness, and scalability, MISAO was compared with the basic SAO, various state-of-the-art optimizers, and some recently developed improved algorithms. The results showed that the overall optimization performance of MISAO is excellent, with Friedman average rankings of 1.80 and 1.82 in the two comparison experiments. In most of the test cases, MISAO delivered more accurate and reliable solutions than its competitors. In addition, the altered algorithm was applied to the selection of hyperparameters for the ICEEMDAN-WLSSVM PV prediction model, and seven neural network models, including WLSSVM, ICEEMDAN-WLSSVM, and MISAO-ICEEMDAN-WLSSVM, were used to predict the PV power under three different weather types. The results showed that the models have high prediction accuracy and stability. The MAPE, MAE and RMSE of the proposed model were reduced by at least 25.3%, 17.8% and 13.3%, respectively. This method is useful for predicting the output power, which is conducive to the economic dispatch of the grid and the stable operation of the power system.

With the rapid proliferation of the Internet of Things (IoT) in recent years, the number of IoT devices has surged exponentially. These devices collect and transmit vast amounts of data, including sensitive information. Encrypting data is a crucial means to prevent unauthorized access and potential misuse. However, the traditional cryptographic schemes offering robust security demand substantial device resources and are unsuitable for lightweight deployments, particularly in resource-constrained IoT devices. On the other hand, with the automotive industry making strides in autonomous driving, self-driving vehicles are beginning to integrate into people’s daily lives. Ensuring the security of autonomous driving systems, particularly in preventing hacker infiltrations, is a paramount challenge currently facing the industry. An emerging lightweight sequence cipher—aiming to strike a balance between security and resource efficiency—has been proposed in this paper based on S-box substitution and arithmetic addition. The designed security threshold is 280. It has been verified that with a slight performance disadvantage, it can reduce memory usage while ensuring the security threshold. The key stream generated by this structure exhibits excellent pseudo-randomness.

Due to the uniqueness of the underwater environment, traditional data aggregation schemes face many challenges. Most existing data aggregation solutions do not fully consider node trustworthiness, which may result in the inclusion of falsified data sent by malicious nodes during the aggregation process, thereby affecting the accuracy of the aggregated results. Additionally, because of the dynamically changing nature of the underwater environment, current solutions often lack sufficient flexibility to handle situations such as node movement and network topology changes, significantly impacting the stability and reliability of data transmission. To address the aforementioned issues, this paper proposes a secure data aggregation algorithm based on a trust mechanism. By dynamically adjusting the number and size of node slices based on node trust values and transmission distances, the proposed algorithm effectively reduces network communication overhead and improves the accuracy of data aggregation. Due to the variability in the number of node slices, even if attackers intercept some slices, it is difficult for them to reconstruct the complete data, thereby ensuring data security.

Slope instability is a common and typical problem of geological hazards, often accompanied by significant losses. So, it is necessary to provide some simple and effective methods to avoid the potential geological hazards of slope instability. It is obvious that the clustering and assessment of slope stability are very crucial. However, the existing clustering and assessment methods in the scenario of single-valued neutrosophic sets (SVNSs) imply some difficulties in engineering applications, such as a lot of collective sampling work, the complex training process, and the selection issue of different types of membership functions. Regarding these problems, this paper proposes an inverse hyperbolic sine similarity measure (IHSSM) of SVNSs and its netting clustering and assessment models for slope stability clustering analysis and evaluation based on the fuzzification process of the true, false, and uncertain Gaussian membership functions for slope sample data. Finally, the proposed clustering and assessment models are applied to the clustering analysis and assessment of 20 slope samples as the case study, and then comparing the results of clustering analysis and stability evaluation of the proposed models with those of the existing relative methods by the 20 slope samples, we verify the validity, consistency, and rationality of the proposed netting clustering and evaluation models.

Abstract As the second most common malignant tumor in the world, lung cancer is a great threat to human health. In the past several decades, the role and mechanism of ncRNAs in lung cancer as a class of regulatory RNAs have been studied intensively. In particular, ncRNAs in body fluids have attracted increasing attention as biomarkers for lung cancer diagnosis and prognosis and for the evaluation of lung cancer treatment due to their low invasiveness and accessibility. As emerging tumor biomarkers in lung cancer, circulating ncRNAs are easy to obtain, independent of tissue specimens, and can well reflect the occurrence and progression of tumors due to their correlation with some biological processes in tumors. Circulating ncRNAs have a very high potential to serve as biomarkers and hold promise for the development of ncRNA-based therapeutics. In the current study, there has been extensive evidence that circulating ncRNA has clinical significance and value as a biomarker. In this review, we summarize how ncRNAs are generated and enter the circulation, remaining stable for subsequent detection. The feasibility of circulating ncRNAs as biomarkers in the diagnosis and prognosis of non-small cell lung cancer is also summarized. In the current systematic treatment of non-small cell lung cancer, circulating ncRNAs can also predict drug resistance, adverse reactions, and other events in targeted therapy, chemotherapy, immunotherapy, and radiotherapy and have promising potential to guide the systematic treatment of non-small cell lung cancer.

Abstract Background & aims This study aimed to establish multivariate prediction models according to a response-guided therapy (RGT) based strategy at baseline and week 12 and 24 of follow-up to predict the functional cure for HBeAg-negative patients with chronic hepatitis B (CHB) treated with pegylated interferonα (PEG-IFNα). Methods A total of 242 HBeAg-negative patients with CHB were treated with PEG-IFNα for 52 weeks and followed up for 24 weeks. Responses at the end of follow-up (EOF) were defined as hepatitis B surface antigen (HBsAg) loss, and patients were defined as either responders or non-responders. Results The three most meaningful predictors were an age ≤ 40 years, alanine aminotransferase (ALT) levels ≤ 40 U/L, and HBsAg levels ≤ 100 IU/mL at baseline; ALT levels ≥ 80 U/L, anti-HBc levels ≤ 8.42 S/CO, and HBsAg levels ≤ 50 IU/mL at week 12; and ALT levels ≥ 40 U/L, anti-HBc levels ≤ 8.46 S/CO, and HBsAg levels ≤ 0.2 IU/mL at week 24. The response rates of patients with a score of 0–1 and 4–5 at baseline, week 12, and 24 were 13.5%, 7.8%, and 11.7%; and 63.6%, 68.1%, and 98.1%, respectively. At week 12, the cumulative scores were 0–2, 3–4, 5–7, and 8–10 (response rates 5.0%, 18.9%, 41.3%, and 71.4%, respectively). At week 24, the cumulative scores were 0–3, 4–6, 7–10, and 11–15 (response rates: 1.3%, 12.3%, 37.0%, and 92.5%, respectively). At baseline, patients with scores of 0–1 were slightly recommended; at week 12, patients with 0–1 or 0–2 cumulative scores were recommended to stop treatment. At week 24, patients with a score of 0–1 or a cumulative score of 0–6 were recommended to stop treatment. Conclusion We established a multi-parameter prediction model for the functional cure of HBeAg-negative patients with CHB treated with PEG-IFNα.

Abstract Background It is of great concern to identify prognostic signatures for the prediction and prediction of esophageal squamous cell carcinoma (ESCC), which is the lethal pathological type of malignancy. Method Bulk RNA sequencing and scRNA-seq data were retrieved from GSE53624, GSE53622, and GSE188900. Disulfidptosis-related differentially expressed genes (DEGs) were identified between disulfidptosis-high score and disulfidptosis-low score groups. Functional annotation of DEGs were analyzed by Gene Ontology (GO). Consistent clustering and co-expression modules were analyzed, and then constructed a risk score model via multivariate Cox regression analysis. Immune infiltration and immunotherapy response analyses were conducted based on risk score. qRT-PCR, colony formation assay, and flow cytometry analysis were conducted in KYSE-150 and TE-1 cell lines. Results Seven genes (CD96, CXCL13, IL2RG, LY96, TPK1, ACAP1, and SOX17) were selected as marker genes. CD96 and SOX17 are independent prognostic signatures for ESCC patients, with a significant correlation with infiltrated immune cells. ESCC patients had worse response to nivolumab in the high-risk group. Through cellular experiments, we found that CD96 expression was associated with apoptosis and cell cycle ESCC cells. Conclusion In a word, the risk score based on disulfidptosis is associated with prognosis and the immune microenvironment, which may direct immunotherapy of ESCC. The key gene of risk score, namely CD96, plays a role in proliferation and apoptosis in ESCC. We offer an insight into the exploration of the genomic etiology of ESCC for its clinical management.

ObjectiveThe primary objective of this study was to examine the levels of serum EZH2 in patients diagnosed with bladder cancer, and subsequently evaluate its potential as a biomarker for both the diagnosis and prognosis of bladder cancer.MethodsBlood samples were obtained from 115 bladder cancer patients and 115 healthy persons. We measured the EZH2 concentrations in the serum of these subjects via enzyme-linked immunosorbent assay (ELISA). To assess the diagnostic performance of serum EZH2 in detecting bladder cancer, we plotted receiver operating characteristic (ROC) curves and calculated their corresponding area under the curve (AUC). We also used the Cox regression model and log-rank test to investigate the correlation between EZH2 levels and clinicopathological characteristics, and survival rates of bladder cancer patients.ResultsSerum EZH2 levels were significantly higher in bladder cancer patients when compared to those in healthy persons. Serum EZH2 levels exhibited a significant correlation with TNM stage, lymph node metastasis, muscle invasion, and tumor size. At a cutoff value of 8.23 ng/mL, EZH2 was able to differentiate bladder cancer patients from healthy persons, with an AUC of 0.87, a sensitivity of 81.31%, and a specificity of 78.42%. High EZH2 levels correlated with poor overall survival rates and progression-free survival rates of bladder cancer patients.ConclusionsSerum EZH2 levels were elevated in bladder cancer patients, and patients with higher serum EZH2 levels exhibited a poorer prognosis. This indicates that serum EZH2 could be a novel biomarker for bladder cancer diagnosis and prognosis. Such findings could improve the prognosis of bladder cancer patients by facilitating early detection and continuous monitoring.

With the development of new urban power systems, the centralized-distributed hierarchical partition management architecture has gradually become a consensus. Existing hierarchical partition methods are mostly static. And if the partition results are determined, it will remain unchanged for a relatively long time. However, the new type power system experiences more frequent and larger fluctuations in power generation and load, requiring dynamic responses to the system’s real-time operation. In this case, traditional partition methods are no longer applicable, and new hierarchical partition methods for system operation need to be adopted. Therefore, this paper proposes a power balance mechanism of urban power system based on dynamic hierarchical partition method, including dynamic hierarchical partition method and corresponding decoupling power balance models. The former can continuously change the results of hierarchical partition according to the real-time state of the power system, so as to reduce the inter-regional liaison cost and improve the economy. The latter improves the independence of the region and the security of the power system through decoupling power balance. Eventually, the proposed method is validated with an modified Hawaii 37-node system.

BackgroundA number of studies have investigated the influencing factors regarding the renewal of contracts associated with Family Doctor Contract Services (FDCS) in different regions of China since it was officially implemented in 2009; however, none of the previous studies have been considered using a nationally representative sample in combination with a meta-analysis.MethodsA multistage stratified sampling method was used to investigate participants’ socio-demographic characteristics, health status, understanding, use, and evaluation of the FDCS, and their willingness to renew contracts in Eastern, Central, and Western China from September to November 2021. We searched the PubMed, Ovid Medline, CNKI, VIP, Wanfang, and SinoMed databases to retrieve previous studies related to the willingness of Chinese residents to renew contracts with their family doctor (FD), and a meta-analysis was performed to systematically summarize the willingness to maintain contracts and influencing factors.ResultsAmong 2,394 residents, 2,122 (88.64%) were willing to renew their contracts. The mixed-effect logistic regression model results demonstrated that residents who (1) preferred primary health service institutions, (2) had a better knowledge of FDCS, (3) were more willing to visit primary health service after signing the contract with FDs, (4) were not intending to change FDs, (5) were satisfied with FDCS, and (6) trusted in FDs reported a higher level of willingness to maintain contracts with FDs. Our meta-analysis confirmed that older age, being married, having chronic diseases, choosing primary medical institutions for the first contact, having a good knowledge of FDCS/FDs, being satisfied with FDCS and the medical skills of FDs, and trusting FDs were all positively associated with residents’ willingness to renew contracts (p

AbstractBackground Observational studies have suggested an association between inflammatory bowel disease [IBD] and psoriasis. However, the detailed genetic basis, causality, and direction of this association remain unclear.Methods Bidirectional two-sample Mendelian Randomization [MR] analysis was conducted using summary statistics from published genome-wide association studies. Bayesian Colocalization and multivariable MR [MVMR] analyses were performed to identify candidate variants and risk genes involved in the shared genetic basis between IBD, psoriasis, and their subtypes.Results Genetically predicted IBD and Crohn’s disease [CD] were associated with an increased risk of psoriasis, psoriasis vulgaris [PsV], and psoriatic arthritis [PsA] (IBD on psoriasis: pooled odds ratio [OR] 1.09, 95% confidence interval [CI] 1.04–1.14, p = .0001; CD on psoriasis: pooled OR 1.10, 95% CI 1.06–1.15, p

ABSTRACTAlthough additive manufacturing (AM) continues to gain widespread adoption, the overhang problem remains a critical issue affecting printing quality. The design of self-supporting structures via topology optimisation approaches has been extensively studied. However, current optimisation research predominantly focuses on 3-axis AM machines, overlooking the more recently developed multi-axis machines. Moreover, the performance sacrifice due to overhang constraints in 3-axis AM can be significant, especially in structures with small volume fractions. To address this, we propose a two-step approach considering overhang constraints for multi-axis AM. This approach begins with a structure optimised using traditional topology optimisation. In the first step, a new optimisation problem determines printing surfaces for the given structure. If the proportion of unprintable elements isn't satisfactory, a second re-optimisation step is carried out to further reduce the unprintable proportion. Several examples demonstrate the effectiveness of the proposed approach. Notably, the significant performance sacrifice associated with the 3-axis AM approach becomes negligible when applying our multi-axis AM-based method.

To realize the all-around assessment of teaching quality in the context of engineering education accreditation, this study proposes a single-valued neutrosophic information entropy and a novel assessment method of teaching effectiveness. In this proposed assessment model, an optimization model is structured based on the minimum information entropy value of single-valued neutrosophic sets (SvNSs). In this study, the primary innovation is that by using the structured model, the attribute weights are extracted from the given deterministic information. The main work of this study is summarized as follows. Firstly, aiming at the engineering certification problem, this study proposes an improved single-value neutrosophic information entropy formula to enhance the effectiveness of the engineering certification results. Secondly, this paper establishes an optimization model based on the minimum information entropy value and provides a method for assigning weight values. Thirdly, this study proposes an improved evaluation method for teaching effect, which provides a novel idea for engineering certification. Thereafter, a case study is presented to demonstrate the effectiveness and practicality of the proposed model.

A neutrosophic number (NN) is a useful mathematical tool in indeterminacy theory. As the mixed form of an intuitionistic fuzzy set and NN, an orthopair neutrosophic number (ONN) can express the true indeterminate degree and the false indeterminate degree. In view of generalized ordered weighted operators, this article presents two generalized ordered weighted operators of ONNs, including an orthopair neutrosophic number generalized ordered weighted average (ONNGOWA) operator and an orthopair neutrosophic number generalized ordered weighted geometric (ONNGOWG) operator, and their characteristics. A multi-attribute decision-making (MADM) model is established by the weighted operation of the ONNGOWA and ONNGOWG operators. Finally, an example on the selection problem of electric vehicle design schemes is given to reflect the effectivity of the proposed MADM model in the scenario of ONNs.

The JRC data collected from a rock mass joint surface difficultly obtain enough large-scale JRC sample data, but small-scale JRC sample data, which usually contain indeterminate and incomplete information due to the limitation of the measurement environment, measurement technology, and other factors. In this case, the existing representation and analysis methods of the JRC sample data almost all lack the measures of confidence levels in the sample data analysis. In this paper, we propose the concept and expression method of confidence neutrosophic number cubic values (CNNCVs), and then establish CNNCVs of joint roughness coefficient (JRC) (JRC CNNCVs) from the limited/small-scale JRC sample data subject to the normal distribution and confidence level of the JRC sample data to analyze the scale effect and anisotropy of JRC values. In the analysis process, the JRC-CNNCVs are first conversed from the JRC sample data (multi-valued sets) in view of their distribution characteristics and confidence level. Next, JRC-CNNCVs are applied to analyze the scale effect and anisotropy of the JRC values by an actual case, and then the effectiveness and rationality of the proposed expression and analysis method using JRC-CNNCVs are proved by the actual case in a JRC multi-valued environment. From a perspective of probabilistic estimation, the established expression and analysis method makes the JRC expression and analysis more reasonable and reliable under the condition of small-scale sample data.

Slope instability is a common geological hazard in open-pit mines, which may cause huge economic losses and casualties. Thus, it is important to cluster and evaluate the stability of slopes effectively. This article proposes a hyperbolic sine similarity measure of single-valued neutrosophic sets (SVNSs) for a netting clustering method and a slope stability evaluation method to cluster and assess the stability of open-pit mine slopes. This study contains the following main content. First, we present a hyperbolic sine similarity measure between SVNSs. Second, slope stability impact factors are fuzzified into SVNSs by the utilization of true, indeterminate, and false membership functions, and then a netting clustering method using the proposed similarity measure is proposed to cluster the stability of open-pit mine slopes. Third, we propose a slope stability evaluation method based on the proposed similarity measure, where we give the SVNS knowledge of risk grades/patterns based on the clustering results of slope stability and then present the similarity measure values between the risk grades/patterns and the slope samples to assess that each slope sample with the larger measure value belongs to the corresponding slope risk grade. Finally, the proposed netting clustering and evaluation methods are applied to the clustering analysis and assessment of 20 open-pit mine slope samples to verify the rationality and effectivity of the proposed approaches in the scenario of SVNSs.

Abstract Background Atherosclerosis is the main pathological change in atherosclerotic cardiovascular disease, and its underlying mechanisms are not well understood. The aim of this study was to explore the hub genes involved in atherosclerosis and their potential mechanisms through bioinformatics analysis. Methods Three microarray datasets from Gene Expression Omnibus (GEO) identified robust differentially expressed genes (DEGs) by robust rank aggregation (RRA). We performed connectivity map (CMap) analysis and functional enrichment analysis on robust DEGs and constructed a protein‒protein interaction (PPI) network using the STRING database to identify the hub gene using 12 algorithms of cytoHubba in Cytoscape. Receiver operating characteristic (ROC) analysis was used to assess the diagnostic potency of the hub genes.The CIBERSORT algorithm was used to perform immunocyte infiltration analysis and explore the association between the identified biomarkers and infiltrating immunocytes using Spearman’s rank correlation analysis in R software. Finally, we evaluated the expression of the hub gene in foam cells. Results A total of 155 robust DEGs were screened by RRA and were revealed to be mainly associated with cytokines and chemokines by functional enrichment analysis. CD52 and IL1RN were identified as hub genes and were validated in the GSE40231 dataset. Immunocyte infiltration analysis showed that CD52 was positively correlated with gamma delta T cells, M1 macrophages and CD4 memory resting T cells, while IL1RN was positively correlated with monocytes and activated mast cells. RT-qPCR results indicate that CD52 and IL1RN were highly expressed in foam cells, in agreement with bioinformatics analysis. Conclusions ​This study has established that CD52 and IL1RN may play a key role in the occurrence and development of atherosclerosis, which opens new lines of thought for further research on the pathogenesis of atherosclerosis.

Abstract Background Peritonitis caused by nontuberculous mycobacteria (NTM) is an infrequent but important complication in patients undergoing peritoneal dialysis (PD). There has been no report of mixed infections with multiple NTM. Peritoneal dialysis-associated peritonitis (PDAP) caused by Mycobacterium abscessus is more common than that caused by M. smegmatis and M. goodii. Case presentation This case concerns a patient with PDAP caused by gram-positive bacilli, which could not be identified at the species level in successive detections of initial peritoneal effluent. Later, M. smegmatis was detected with no sensitivity results in bacterial culture. However, metagenomic next-generation sequencing (mNGS) and first whole-genome sequences indicated that there were three species coexisting in the culture, including M. smegmatis (24,708 reads), M. abscessus (9224 reads), and M. goodii (8305 reads). This is the first case of PDAP with specific evidence that conventional detection methods isolated a poorly pathogenic NTM, whereas mNGS and first whole-genome sequences identified multiple NTM. Pathogenic bacteria might not be detected using conventional methods due to their lower abundance. This case report is the first description of mixed infections with more than two species of NTM during PDAP. Conclusions PDAP caused by multiple NTM is rare, and the diagnosis is difficult. When NTM are isolated by conventional tests in patients who are suspected of infection, clinicians should be vigilant, and further tests should be performed to determine the presence of rare or even previously unknown bacteria, for which the quantity is relatively low, but the pathogenicity is high. The rare pathogen may be a primary agent in causing such complications.

Underwater acoustic sensor networks have a wide range of applications in both civil and military fields, but the complex and changing underwater environment makes them vulnerable to multiple security threats. Trust mechanisms are effective ways to enhance network security and reliability. In order to improve the accuracy of trust evaluation and the detection rate of abnormal nodes, this paper proposes an adaptive trust evaluation model based on fuzzy logic. This model adopts a variable weight fuzzy comprehensive evaluation algorithm to dynamically adjust the weights of three direct trust indicators to ensure the accuracy of direct trust evaluation. Then, it uses fuzzy closeness to eliminate unreliable recommendation trust and adjusts the weight of recommendation trust through deviation to improve the accuracy of indirect trust. The simulation results show that the model can effectively improve the accuracy of trust evaluation and the detection rate of abnormal nodes. Especially when the link quality is unstable, the success rate of detecting abnormal nodes in this model is improved by more than 10% compared with the existing trust model.

Rotavirus (RV) is the main pathogen that causes severe diarrhea in infants and children under 5 years of age. No specific antiviral therapies or licensed anti-rotavirus drugs are available. It is crucial to develop effective and low-toxicity anti-rotavirus small-molecule drugs that act on novel host targets. In this study, a new anti-rotavirus compound was selected by ELISA, and cell activity was detected from 453 small-molecule compounds. The anti-RV effects and underlying mechanisms of the screened compounds were explored. In vitro experimental results showed that the small-molecule compound ML241 has a good effect on inhibiting rotavirus proliferation and has low cytotoxicity during the virus adsorption, cell entry, and replication stages. In addition to its in vitro effects, ML241 also exerted anti-RV effects in a suckling mouse model. Transcriptome sequencing was performed after adding ML241 to cells infected with RV. The results showed that ML241 inhibited the phosphorylation of ERK1/2 in the MAPK signaling pathway, thereby inhibiting IκBα, activating the NF-κB signaling pathway, and playing an anti-RV role. These results provide an experimental basis for specific anti-RV small-molecule compounds or compound combinations, which is beneficial for the development of anti-RV drugs.

Abstract Background The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) is still under investigation as adjuvant treatment for early-stage disease. Here, we performed a meta-analysis to evaluate the efficacy of adjuvant EGFR-TKI versus non-EGFR-TKI treatment in patients with completely resected non-small cell lung cancer (NSCLC) harboring EGFR mutation. Methods Two investigators independently extracted data from databases. A meta-analysis was performed following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The protocol was registered in PROSPERO (ID: CRD42022316481). The primary outcome was disease-free survival (DFS) in patients with EGFR mutation, measured as the hazard ratio (HR). Other outcomes (of subgroup analyses) included overall survival (OS) and DFS. Results After the systematic screening, eight studies with a total of 3098 patients with stage IB–IIIA NSCLC were included. The results show that in patients with EGFR mutation, the DFS in the adjuvant EGFR-TKI group was significantly superior to that in the control group, with a HR of 0.47 (95% confidence interval [CI]: 0.30–0.74; P = 0.001). In subgroup analyses of DFS, the benefit was observed in the EGFR-TKI group versus the chemotherapy group (HR 0.50, 95% CI 0.30–0.84; P = 0.009), the EGFR-TKI combined with chemotherapy group versus the chemotherapy group (HR 0.37, 95% CI 0.16–0.85; P = 0.02), and in stage IIA–IIIA NSCLC (HR 0.45, 95% CI 0.27–0.74; P = 0.002). However, the benefit of DFS did not translate into improved OS in the whole population (HR 0.79, 95% CI 0.54–1.14; P = 0.20). Conclusion EGFR-TKIs prolonged DFS but not OS in patients with completely resected stage II–IIIA NSCLC harboring EGFR mutation. Longer follow-ups and new clinical trials that can result in changes in clinical practice are needed.

As a generalization of trapezoidal fuzzy neutrosophic numbers (TFNNs), credibility trapezoidal fuzzy neutrosophic numbers (C-TFNNs) can independently describe true, false, and indeterminate membership degrees and their credibility levels in uncertain and inconsistent scenarios. Since the true, false, and indeterminate membership degrees are closely related to their credibility levels, C-TFNN can ensure the credibility of TFNN, which shows its clear merit. However, C-TFNNs cannot expresses the interval membership degrees of the truth, falsity and indeterminacy and the uncertain credibility levels, which are produced due to human cognitive vagueness, incompleteness, and uncertainty. Furthermore, existing decision models of C-TFNNs cannot perform such a DM issue with both ITFNNs and uncertain credibility levels, which reveals a gap. To compensates for this gap. this paper extends C-TFNNs to credibility interval TFNNs (C ITFNNs), which strengthens the expression capability of uncertain information. Then, the operational laws and score function of C-ITFNNs are defined to solve the aggregation and sorting issues of C-ITFNNs in decision-making (DM) problems. Subsequently, the C-ITFNN weighted geometric averaging (C-ITFNNWGA) and C-ITFNN weighted arithmetic averaging (C ITFNNWAA) operators are proposed in view of operational laws of C-ITFNNs. Furthermore, a multi-attribute DM model is established in terms of the two aggregation operators and the score function in the C-ITFNN circumstance. Finally, a DM case of landslide control design schemes is used to reveal the applicability of the proposed DM model in the C-ITFNN scenario. By comparative analysis, the main superiority of our new DM model is that it not only compensates for the gap of existing DM models, but also is more reliable and versatile than existing DM models.

Recently, colleges/universities have paid a lot of attention to the teaching quality evaluation (TQE) of teachers in China. TQE is an essential way to improve teachers' teaching ability and quality in the teaching process. Then, the TQE of teaching supervisors is a multi-attribute decision-making (MADM) problem with vague, inconsistent, and indeterminate information. The simplified neutrosophic indeterminate set/element (SNIS/SNIE) is an appropriate form to express the indeterminate decision-making information in the TQE process. Therefore, this article presents an improved ranking method based on maximizing deviations principle and technique for order of preference by similarity (TOPSIS) for SNIS and applies it to evaluate teachers' teaching quality. First, the Hamming distance between two SNIEs is defined. Then, attribute weights are obtained by maximizing deviation method and the TOPSIS method-based decision-making model is developed for the MADM applications with unknown attribute weights. Finally, we perform the developed MADM model for a TQE case and compare it with existing related models to indicate the feasibility and rationality of the proposed model with unknown attribute weights in the SNIE circumstance.